Ventilation triggering using change-point detection

ABSTRACT

The systems and methods provide for novel a triggering mode that allows the patient to trigger or initiate the delivery of a breath during ventilation on a ventilator. Further, the systems and methods provide for triggering ventilation utilizing a statistical trigger mode. Additionally, the systems and methods provide for analyzing and/or displaying information related to a potential change in a triggering threshold for a currently utilized breath type.

INTRODUCTION

Medical ventilator systems have long been used to provide ventilatory and supplemental oxygen support to patients. These ventilators typically comprise a source of pressurized oxygen which is fluidly connected to the patient through a conduit or tubing. As each patient may require a different ventilation strategy, modern ventilators can be customized for the particular needs of an individual patient. For example, several different ventilator modes or settings have been created to provide better ventilation for patients in various different scenarios, such as mandatory ventilation modes, assist control ventilation modes, and spontaneous modes.

Triggering

This disclosure describes novel triggering systems and methods that allow the patient to trigger or initiate the delivery of a breath during ventilation on a ventilator. Further, this disclosure describes systems and methods for triggering ventilation utilizing a statistical trigger mode. This disclosure also describes novel systems and methods for analyzing and/or displaying the ramifications of a potential change in a triggering threshold for a currently utilized breath type.

In part, this disclosure describes a method for ventilating a patient with a ventilator. The method includes:

-   -   monitoring a parameter signal during exhalation;     -   determining a stable portion of exhalation based at least on the         parameter signal for a current computational cycle;     -   setting an initial probability for each of a null hypothesis and         a trigger hypothesis based on a current exhalation time;     -   calculating a mean of the parameter signal based on a         predetermined set of parameter signals for a most recent set of         computational cycles;     -   updating a noise estimate based at least on the mean of the         parameter signal;     -   calculating a residual for the null hypothesis and the trigger         hypothesis based at least on the parameter signal for the         current computational cycle;     -   calculating a first probability for the null hypothesis and         calculating a second probability for the trigger hypothesis for         the parameter signal for the current computational cycle based         on the initial probability, the noise estimate, and the         residual;     -   comparing the first probability and the second probability to a         threshold; and     -   controlling ventilation delivered to the patient by the         ventilator based on the comparison.

In part, this disclosure describes a method for ventilating a patient with a ventilator. The method includes:

-   -   monitoring a parameter signal during an exhalation;     -   determining a stable portion of exhalation based at least on the         parameter signal for a current computational cycle;     -   calculating an initial predicted parameter signal of a next         computational cycle and an initial covariance for the initial         predicted parameter signal for the parameter signal for the         current computational cycle;     -   calculating a post predicted parameter signal of the next         computational cycle and a post covariance for the post predicted         parameter signal for a first and second derivative of the         parameter signal of the parameter signal for the current         computational cycle;     -   determining that a run threshold has been met based on at least         one of a current exhalation time, the initial covariance, and         the post covariance;     -   updating a noise estimate based at least on a single value of         the covariance utilized in calculating at least one of the         initial covariance and the post covariance;     -   calculating a residual for each of a null hypothesis and a         trigger hypothesis based at least on the parameter signal for         the current computational cycle;     -   calculating a first probability for the null hypothesis and         calculating a second probability for the trigger hypothesis for         the parameter signal for the current computational cycle based         at least on a predicted parameter signal, the noise estimate,         and the residual;     -   comparing the first probability and the second probability to a         trigger threshold; and     -   delivering inspiration to the patient when the second         probability meets the trigger threshold.

The disclosure additionally describes a ventilation system. The ventilator system includes a pressure generating system, a ventilation tubing system, at least one sensor, and a trigger module. The pressure generating system is configured to generate a flow of breathing gas. The ventilation tubing system includes a patient interface for connecting the pressure generating system to a patient. The at least one sensor is operatively coupled to at least one of the pressure generating system, the patient, and the ventilation tubing system. The trigger module determines a first probability for a null hypothesis and a second probability for a trigger hypothesis based on a monitored parameter signal. The trigger module triggers inspiration when the second probability meets a trigger threshold.

These and various other features as well as advantages which characterize the systems and methods described herein will be apparent from a reading of the following detailed description and a review of the associated drawings. Additional features are set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the technology. The benefits and features of the technology will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawing figures, which form a part of this application, are illustrative of embodiments of systems and methods described below and are not meant to limit the scope of the disclosure in any manner, which scope shall be based on the claims.

FIG. 1 illustrates an embodiment of a ventilator.

FIG. 2A illustrates an embodiment of a method for triggering inspiration during ventilation of a patient on a ventilator utilizing a constant statistical trigger mode.

FIG. 2B illustrates an embodiment of a method for triggering inspiration during ventilation of a patient on a ventilator utilizing a variable statistical trigger mode.

FIG. 3 illustrates an embodiment of a method for analyzing and/or displaying potential trigger information for a delivered breath type during ventilation of a patient on a ventilator.

FIG. 4 illustrates an embodiment of a graph of a constant positive-end expiratory pressure (PEEP) signal and a graph of a variable PEEP signals during a single breath while ventilating a patient on a ventilator.

FIG. 5 illustrates an embodiment of a graph of an exhalation flow signal during one breath while ventilating a patient on a ventilator.

FIG. 6 illustrates an embodiment of a graph showing an improved trigger detection speed for a constant statistical trigger mode when compared to a traditional flow trigger breath type utilized while ventilating a patient on a ventilator.

FIG. 7 illustrates an embodiment of a graph of pressure versus time for an exhalation with a constant PEEP and illustrates a corresponding graph of the calculated probabilities for a null and trigger hypotheses by a constant ST mode versus time for the same exhalation.

FIG. 8 illustrates an embodiment of a graph of pressure versus time for an exhalation with a constant PEEP and illustrates a corresponding graph of the calculated probability for a null and trigger hypotheses by a constant ST mode versus time for the same exhalation.

FIG. 9 illustrates an embodiment of a graph showing an improved trigger detection speed for a variable statistical trigger mode when compared to a traditional flow trigger breath type utilized while ventilating a patient on a ventilator.

FIG. 10 illustrates an embodiment of a graph of pressure versus time for an exhalation with a constant PEEP and illustrates a corresponding graph of the calculated probability of a trigger hypothesis by a constant ST mode versus time for the same exhalation.

FIG. 11 illustrates an embodiment of a graph of a predicted signal and its covariance for a measured pressure signal, a graph of a second predicted signal and its covariance for the first derivative of the measured pressure signal, and a graph of a third predicted signal and its covariance for the second derivative of the measured pressure signal for the same exhalation as illustrated in FIG. 10.

FIG. 12 illustrates an embodiment of a patient flow waveform displaying active indicators and potential indicators.

FIG. 13 illustrates an embodiment of a graph of a patient pressure waveform displaying active indicators and potential indicators.

FIG. 14 illustrates an embodiment of a graph illustrating a potential decrease in triggering times for different potential trigger settings.

DETAILED DESCRIPTION

Although the techniques introduced above and discussed in detail below may be implemented for a variety of medical devices, the present disclosure will discuss the implementation of these techniques in the context of a medical ventilator for use in providing ventilation support to a human patient. A person of skill in the art will understand that the technology described in the context of a medical ventilator for human patients could be adapted for use with other systems such as ventilators for non-human patients and general gas transport systems.

Medical ventilators are used to provide a breathing gas to a patient who may otherwise be unable to breathe sufficiently. In modern medical facilities, pressurized air and oxygen sources are often available from wall outlets. Accordingly, ventilators may provide pressure regulating valves (or regulators) connected to centralized sources of pressurized air and pressurized oxygen. The regulating valves function to regulate flow so that respiratory gas having a desired concentration of oxygen is supplied to the patient at desired pressures and rates. Ventilators capable of operating independently of external sources of pressurized air are also available.

As each patient may require a different ventilation strategy, modern ventilators can be customized for the particular needs of an individual patient. For example, several different ventilator modes or settings have been created to provide better ventilation for patients in various different scenarios, such as mandatory ventilation modes, spontaneous modes, and assist control ventilation modes. Assist control and spontaneous modes allow a spontaneously breathing patient to trigger inspiration during ventilation.

The response performance of a medical ventilator to a patient trigger from exhalation into inhalation phase represents an important characteristic of a medical ventilator. A ventilator's trigger response impacts the patient's work of breathing and the overall patient-ventilator synchrony. The trigger response performance of a ventilator is a function of a patient's inspiratory behavior (breathing effort magnitude and timing characteristics) as well as the ventilator's gas delivery dynamics and flow control parameters (actuator response, dead bands, etc.).

In conventional triggering modes, a patient's inspiratory trigger is detected based on a comparison of flow and/or pressure signal generated by the patient's inspiratory effort to a predetermined threshold. Having an effective trigger is critical for success in spontaneous (i.e. patient-initiated) ventilation modes and/or breath types. The effectiveness of a trigger can be judged by two major factors: sensitivity to patient initiation and false trigger rate. A common trigger implementation, whether pressure- or flow-based, initiates a spontaneous breath when the pressure or flow crosses a clinician-set threshold. Setting the threshold too low reduces patient effort to trigger a new breath but increases the false trigger rate whereas increasing the threshold will reduce the false trigger rate, but may cause the patient to struggle (i.e., increased work of breathing). If an effective midpoint between these two factors is not found, it may force the patient back on ventilator-initiated breathing, in many cases requiring sedation.

Further, missed inspiration triggering is particularly prevalent during the ventilation of chronic obstructive pulmonary disease patients (COPD). COPD patients demand another breath before they have fully exhaled. As a result, traditional flow triggering modes are not able to detect patient efforts effectively even with the best optimized trigger thresholds.

Accordingly, the systems and methods described herein provide for improved inspiration triggering. The improved inspiration triggering reduces or prevents false triggering even when a low trigger threshold is utilized. This new ventilator synchronization mechanism is referred to herein as the statistical trigger mode (“ST mode”). While the ST mode is referred to herein as a mode, it may also be referred to as a triggering type, breath type, supplemental breath type, or supplemental mode because the ST mode is utilized in conjunction with or in addition to any spontaneous mode or assist control mode of ventilation running any suitable breath type. The ST mode improves ventilator synchrony by improving inspiration trigger detection by utilizing a statistical approach. For example, the ST mode decreases or prevents false triggers and increases the speed of the trigger detection. For example, conventional inspiration triggering modes can require 300 ms or more to detect a patient trigger in patients. The ST mode may decrease this detection time by as much as 270 ms and on average from 80 ms to 181 ms.

Additionally, the systems and methods described herein provide for analyzing and/or displaying the ramifications of a potential change in a trigger threshold for a currently utilized breath type allowing a clinician to view the ramifications of a change in a trigger threshold of a utilized breath type without implementing the change. As such, these systems and methods allow a clinician to determine if the potential trigger threshold would improve trigger patient-ventilator synchrony. The clinician can review the provided data regarding the potential change in trigger and see if that change would increase or decrease false triggers and/or the speed of trigger response time. This information provides the clinician with the information needed to easily and confidently determine if a trigger sensitivity for a current breath type should be changed and by what degree. Previously, the clinician would have to implement the change and watch the patient's response to determine if a change in trigger threshold would be beneficial for the patient, which could increase ventilator asynchrony and the patient's work of breathing.

FIG. 1 is a diagram illustrating an embodiment of an exemplary ventilator 100. The exemplary ventilator 100 illustrated in FIG. 1 is connected to a human patient 150. Ventilator 100 includes a pneumatic system 102 (also referred to as a pressure generating system 102) for circulating breathing gases to and from patient 150 via the ventilation tubing system 130, which couples the patient 150 to the pneumatic system 102 via an invasive (e.g., endotracheal tube, as shown) or a non-invasive (e.g., nasal mask) patient interface 180.

Ventilation tubing system 130 (or patient circuit 130) may be a two-limb (shown) or a one-limb circuit for carrying gases to and from the patient 150. In a two-limb embodiment, a fitting, typically referred to as a “wye-fitting” 170, may be provided to couple the patient interface 180 (shown as an endotracheal tube in FIG. 1) to an inspiratory limb 132 and an expiratory limb 134 of the ventilation tubing system 130.

Pneumatic system 102 may be configured in a variety of ways. In the present example, pneumatic system 102 includes an expiratory module 108 coupled with the expiratory limb 134 and an inspiratory module 104 coupled with the inspiratory limb 132. Compressor, accumulator and/or other source(s) of pressurized gases (e.g., air, oxygen, and/or helium) is coupled with inspiratory module 104 and the expiratory module 108 to provide a gas source for ventilatory support via inspiratory limb 132. The pneumatic system 102 may include a variety of other components, including mixing modules, valves, tubing, accumulators, filters, etc.

The inspiratory module 104 is configured to deliver gases to the patient 150 and/or through the inspiratory limb 132 according to prescribed ventilatory settings. The inspiratory module 104 is associated with and/or controls an inspiratory valve for controlling gas delivery to the patient 150 and/or gas delivery through the inspiratory limb 132. In some embodiments, inspiratory module 104 is configured to provide ventilation according to various ventilator modes, such as mandatory, spontaneous, and assist modes.

The expiratory module 108 is configured to release gases from the patient's lungs according to prescribed ventilatory settings. The expiratory module 108 is associated with and/or controls an expiratory valve for releasing gases from the patient 150. Further, the expiratory module 108 and/or the inspiratory module 104 may instruct the pressure generating system 102 and/or the inspiratory module 104 to deliver a base flow during exhalation. In an alternative embodiment, the pressure generating system 102 may instruct the inspiratory module 104 to deliver a base flow during exhalation.

The ventilator 100 may also include one or more sensors 107 communicatively coupled to ventilator 100. The sensors 107 may be located in the pneumatic system 102, ventilation tubing system 130, patient interface 180, and/or on the patient 150. The embodiment of FIG. 1A illustrates a sensor 107 in pneumatic system 102.

Sensors 107 may communicate with various components of ventilator 100, e.g., pneumatic system 102, other sensors 107, expiratory module 108, inspiratory module 104, processor 116, controller 110, trigger module 115, potential trigger module 118, and any other suitable components and/or modules. In one embodiment, sensors 107 generate output and send this output to pneumatic system 102, other sensors 107, expiratory module 108, inspiratory module 104, processor 116, controller 110, trigger module 115, potential trigger module 118, and any other suitable components and/or modules.

Sensors 107 may employ any suitable sensory or derivative technique for monitoring and/or measuring one or more patient parameters or ventilator parameters associated with the ventilation of a patient 150 and generate parameter signals. The parameter signals are sent or communicated to other components and/or modules of the ventilator 100. A module as utilized herein is a command and/or control computing devices that may include memory, one or more processors, storage, and/or other components of the type commonly found in command and/or control computing devices. In some embodiments, the signals are sent to the controller 110, processor 116, trigger module 115, and/or potential trigger module 118. In some embodiments, the sensors may generate parameter signals that include parameter measurements every computational cycle. In some embodiments, the computational cycle is every 5 ms. Any suitable computation cycle for a ventilator 100 may be utilized as would be known by a person of skill in the art. In other embodiments, the computation cycle may be anywhere from 2 ms to 20 ms. Sensors 107 may detect changes in patient parameters indicative of patient inspiratory or expiratory triggering, for example. Any sensory device useful for monitoring changes in measurable parameters during ventilatory treatment may be employed in accordance with embodiments described herein.

Sensors 107 may be placed in any suitable location, e.g., within the ventilatory circuitry or other devices communicatively coupled to the ventilator 100. Further, sensors 107 may be placed in any suitable internal location, such as, within the ventilatory circuitry or within components or modules of ventilator 100. For example, sensors 107 may be coupled to the inspiratory and/or expiratory modules 104, 108 for detecting changes in, for example, circuit pressure and/or flow. In other examples, sensors 107 may be affixed to the ventilatory tubing or may be embedded in the tubing itself. According to some embodiments, sensors 107 may be provided at or near the lungs (or diaphragm) for detecting a pressure in the lungs. Additionally or alternatively, sensors 107 may be affixed or embedded in or near wye-fitting 170 and/or patient interface 180. In other embodiments, the sensors 107 are placed in other suitable locations for determining patient triggers based on the selected trigger or breath type. For example, during a neural trigger or breath type, sensors 107 may require endoscopic placement to detect diaphragm neural stimulus.

As should be appreciated, with reference to the Equation of Motion, the ideal gas law, and/or Vander Waals equation, ventilatory parameters are highly interrelated and, according to embodiments, may be either directly or indirectly monitored. That is, parameters may be directly monitored by one or more sensors 107, as described above, or may be indirectly monitored or estimated by derivation according to the Equation of Motion, the ideal gas law, Vander Waals equation, and/or other known relationships.

Controller 110 is operatively coupled with pneumatic system 102, signal measurement and acquisition systems, and an operator interface 120 that may enable an operator to interact with the ventilator 100 (e.g., change ventilator settings, select operational modes, view monitored parameters, etc.). In some embodiments, the controller 110 is remote from the ventilator 100 and communicationally coupled to the ventilator 100.

In one embodiment, the operator interface 120 of the ventilator 100 includes a display 122 communicatively coupled to ventilator 100. Display 122 provides various input screens, for receiving clinician input, and various display screens, for presenting useful information to the clinician. In one embodiment, the display 122 is configured to include a graphical user interface (GUI). The GUI may be an interactive display, e.g., a touch-sensitive screen or otherwise, and may provide various windows and elements for receiving input and interface command operations. Alternatively, other suitable means of communication with the ventilator 100 may be provided, for instance by a wheel, keyboard, mouse, or other suitable interactive device. Thus, operator interface 120 may accept commands and input through display 122.

Display 122 may also provide useful information in the form of various ventilatory data regarding the physical condition of a patient 150. The useful information may be derived by the ventilator 100, based on data collected by a processor 116, and the useful information may be displayed to the clinician in the form of graphs, wave representations, pie graphs, text, or other suitable forms of graphic display. For example, patient data may be displayed on the GUI and/or display 122. Additionally or alternatively, patient data may be communicated to a remote monitoring system coupled via any suitable means to the ventilator 100 to display useful information in the form of various ventilatory data regarding the ventilator settings and the physical condition of a patient 150. In some embodiments, the display 122 may illustrate active indicators, potential indicators, different statistics, difference graphs, parameter signals, initial probabilities, mean parameter signals, noise estimates, residuals, calculated probabilities, trigger thresholds, run thresholds, predicted signal parameters, and/or any other information known, received, or stored by the ventilator 100.

In some embodiments, controller 110 includes memory 112, one or more processors 116, storage 114, and/or other components of the type commonly found in command and control computing devices. Controller 110 may further include a trigger module 115 and a potential trigger module 118, as illustrated in FIG. 1. In alternative embodiments, the trigger module 115 and/or potential trigger module 118 are located in other components of the ventilator 100, such as in the pressure generating system 102 (also known as the pneumatic system 102).

The memory 112 includes non-transitory, computer-readable storage media that stores software that is executed by the processor 116 and which controls the operation of the ventilator 100. In an embodiment, the memory 112 includes one or more solid-state storage devices such as flash memory chips. In an alternative embodiment, the memory 112 may be mass storage connected to the processor 116 through a mass storage controller (not shown) and a communications bus (not shown). Although the description of computer-readable media contained herein refers to a solid-state storage, it should be appreciated by those skilled in the art that computer-readable storage media can be any available media that can be accessed by the processor 116. That is, computer-readable storage media includes non-transitory, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. For example, computer-readable storage media includes RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer.

Ventilators 100, during assist and spontaneous modes of operation, trigger inspiration in response to a detected patient effort. The trigger module 115 detects patient efforts based on one or more parameter signals depending upon the selected trigger mode and triggers inspiration in response to a detected patient effort. Different trigger modes utilize different methods for determining a patient effort. For example, in some embodiment, the patient's inspiratory trigger is detected based on a comparison of a flow and/or pressure signal or a change in a flow and/or pressure signal generated by the patient's inspiratory effort to a predetermined threshold. In other embodiments, a patient's neural signals related to his or her inspiratory efforts are compared to a predetermined threshold and utilized to detect a patient's inspiratory effort. In further embodiments, a patient's intrapleural pressure is monitored and compared to a predetermined threshold to detect a patient's inspiratory effort. In other embodiments, a statistical analysis of measured pressure or flow (or other signal) is monitored and compared to a predetermined probability threshold (an ST mode) to detect a patient's inspiratory effort.

Additionally, as discussed above, each of these different trigger modes requires the use of a trigger threshold. The more sensitive the trigger threshold, the less patient effort a patient has to exhibit in order for the trigger module 115 to detect a patient effort. The less sensitive the trigger threshold, the more patient effort the patient has to exhibit in order for the trigger module 115 to detect a patient effort. As discussed above, the response performance of a medical ventilator to a patient trigger from exhalation into inhalation phase represents an important characteristic of a medical ventilator. A ventilator's trigger response impacts the patient's work of breathing and the overall patient-ventilator synchrony. Having an effective trigger is critical for success in spontaneous (i.e. patient-initiated) ventilation modes. The effectiveness of a trigger can be judged by two major factors: sensitivity to patient initiation and false trigger rate. Setting the threshold too low reduces patient effort to trigger a new breath but increases the false trigger rate whereas increasing the threshold will reduce the false trigger rate, but may cause the patient to struggle. If an effective midpoint between these two factors is not found, it may force the patient back on ventilator-initiated breathing, in many cases requiring sedation.

In order to improve the effectiveness of the trigger, the trigger module 115 may implement a ST mode. The ST mode reduces or prevents auto-triggering even when a low trigger threshold is utilized. The ST mode improves ventilator synchrony by improving inspiration trigger detection. As such, in some embodiments, the ST mode decreases or prevents false triggers and increases the speed of the trigger detection. For example, conventional inspiration flow or pressure triggering modes require about 300 ms or more to detect a patient trigger. The ST mode may decrease this trigger detection time by as much as 270 ms and on average from 80 ms to 181 ms. For example, FIGS. 6 and 9 illustrate an embodiment of graphs showing the improved trigger detection speed for the ST mode when compared to a traditional flow triggering breath type.

Additionally, regardless of the trigger mode utilized by the trigger module 115, the ventilator 100 may utilize a potential trigger module 118 to allow a clinician to refine or find the best trigger threshold for any utilized trigger mode or breath type without having to actually implement a new trigger threshold. In this embodiment, the ventilator 100 utilizes an active trigger threshold (also referred to herein as active trigger setting) to trigger ventilation with the trigger module 115 and utilizes a potential trigger threshold (also referred to herein as a potential trigger setting) to determine where the trigger module 115 would have triggered inspiration if the potential trigger threshold had been utilized to trigger inspiration. Providing this potential trigger information to the clinician and/or providing an analysis of this potential trigger information in comparison to the active trigger, allows a clinician to easily and confidently determine if a potential trigger setting would be beneficial or not for the patient.

The trigger module 115 receives and/or determines an inspiration trigger threshold (also referred to herein as an active trigger setting). In some embodiments, the trigger module 115 receives the active trigger setting from operator input. In other embodiments, the trigger module 115 determines an active trigger setting based on ventilator and/or patient parameters.

The mechanisms for detecting a patient effort by the trigger module 115 for known breath types and/or trigger modes are understood by a person of skill in the art and are therefore, not discussed herein in detail. It is understood by a person of skill in the art, that the trigger module 115 may be utilized to detect patient efforts utilizing these known trigger types or modes (e.g. flow triggering, neural triggering, intrapleural pressure triggering, pressures triggering, etc.). However, the mechanisms for detecting a patient effort utilizing the ST mode are novel and will therefore be discussed in detail below.

The ST mode monitors how a parameter signal statistically changes as opposed to comparing the signal itself against a predefined threshold. To accomplish this, the ST mode utilizes a class of algorithms commonly referred to as change detection, change-point detection, or residual change detection algorithms. In some embodiments, the ST mode utilizes an adopted version of the Multiple-Hypothesis Shiryayev Sequential Probability Ratio Test (MHSSPRT). The ST mode estimates the probability of whether the null hypothesis and the trigger hypothesis are true. The null hypothesis is analogous to the case where the patient has not yet initiated a breath whereas the trigger hypothesis represents the initiation of a breath. During the ST mode the trigger module 115 updates the noise estimate, calculates the filter residuals, and calculates the probability of each hypothesis. The trigger module 115 determines a patient effort during the ST mode when the probability of the trigger hypothesis exceeds a predefined threshold.

One of the advantages of the ST mode is that the trigger module 115 monitors an estimated value of the parameter signal that is less sensitive to noise, allowing a more sensitive trigger threshold to be set without increasing the false trigger rate. The quality of the monitored signal varies greatly breath-to-breath, especially after changes to the ventilator settings. To account for this quality variation, the ST mode characterizes the noise of the signal and uses this noise to ensure the filter is robust in the presence of increased noise.

In order to calculate the residuals effectively, the trigger module 115 must have a method of predicting what the parameter signal looks like when a breath has not been initiated, i.e. the null signal. Depending on the breathing mode and lung characteristics, predicting this can be trivial or more involved. If the parameter signal is relatively constant after exhaling, no projection algorithm is required; the signal value from the previous time step can be carried forward. A pressure signal where the pressure measurements settle quickly and remain fairly constant during exhalation is an example of a constant exhalation signal. If the signal does not remain constant during exhalation, a prediction model such as a Kalman Tracking Filter (KTF) can be utilized during the ST mode by the trigger module 115 to project the null signal. As such, two different kinds of ST mode may be utilized by the trigger module 115. A constant ST mode may be utilized by the trigger module 115 when the signal remains fairly constant and a variable ST mode may be utilized by the trigger module 115 when the signal does not remain constant during exhalation. FIG. 4 illustrates an embodiment of a graph of the two different types of pressure signals. Graph 402 illustrates a pressure signal that has a substantially constant PEEP (e.g., a parameter signal that is relatively constant during exhalation). The trigger module 115 would utilize the constant ST mode to trigger ventilation for the parameter signal illustrated in graph 402. Graph 404 illustrates a pressure signal with a descending PEEP (a parameter signal that is not constant during exhalation). The trigger module 115 would utilize the variable ST mode to trigger ventilation for the parameter signal illustrated in graph 404.

In alternative embodiments, the KTF is utilized during other triggering modes to filter monitored signals for triggering. For example, the KTF could be applied to flow signals in a flow triggering breath type or pressure signals in a pressure triggering breath type.

During the ST mode, the trigger module 115 determines a stable portion of exhalation based at least on a measurement of the parameter signal for a current computational cycle. A current computational cycle as utilized herein is the most recent computation cycle. A next computational cycle as utilized herein is a future computation cycle that will occur directly after the current computation cycle. The stable portion of exhalation is the portion of exhalation when a patient 150 is contributing very little or no flow and/or pressure through the expiratory limb 134 and is prior to the beginning of inspiration as illustrated in FIG. 5. The stable portion of exhalation usually occurs right after an active portion of exhalation where the flow and/or pressure vary significantly. FIG. 5 illustrates an embodiment of a graph 500 of exhalation flow during one breath while ventilating a patient 150 on a ventilator 100. FIG. 5 further illustrates the active portion and the stable portion of an exhalation.

The trigger module 115 may utilize any suitable method for determining a stable portion of exhalation. In some embodiments, in order to determine the stable portion of exhalation, the trigger module 115 monitors one or more parameter signals, such as exhalation pressure and/or exhalation flow. In some embodiments, the exhaled flow and/or pressure is monitored with an expiratory flow sensor. In other embodiments, the exhaled flow and/or pressure is monitored with an exhalation pressure sensor. In some embodiments, the trigger module 115 monitors the exhalation flow every computation cycle. In some embodiments, the trigger module 115 determines a stable portion of exhalation when the slope of the exhalation flow is zero, about zero, or less than a predetermined threshold. In other embodiments, the trigger module 115 determines a stable portion of exhalation by determining if the difference between the maximum exhalation pressure and the minimum exhalation pressure is less that 1.5 cm of H₂O ((Max(P_(e))−Min(P_(e)))<1.5 cm H₂O) and/or determines if the difference between maximum exhalation flow and minimum exhalation flow is less than 1.5 LPM ((Max(Q_(e))−Min(Q_(e)))<1.5 LPM) during a predetermined interval. In some embodiments, the maximum and minimum values are calculated and compared based on the flow and pressure data saved in a 10-point buffer (e.g., pertaining to a 50 ms predetermined interval). Maximum and minimum values for the moving 10-point windows are tracked each computation cycle during exhalation. If the difference between the maximum exhalation pressure and the minimum exhalation pressure is less that 1.5 cm of H₂O and/or the difference between maximum exhalation flow and minimum exhalation flow is less than 1.5 LPM, then the trigger module 115 determines that the patient 150 is in the stable portion of exhalation (or that active exhalation has been completed). If the difference between the maximum exhalation pressure and the minimum exhalation pressure is not less than 1.5 cm of H₂O and/or the difference between maximum exhalation flow and minimum exhalation flow is not less than 1.5 LPM for a current computation cycle, then the trigger module 115 determines that the patient 150 is not in the stable portion of exhalation (or is in the active portion of the exhalation).

The minimum pressure and flow values of 1.5 LPM are based on the characteristics of an exemplary ventilator. Other values and different pressure and flow levels may be used as appropriate based on the ventilator being currently utilized. Further, depending on the utilized ventilator, the flow and pressure stability thresholds may not necessarily have the same magnitude. The thresholds are selected to provide minimal respiratory activity by the patient.

The embodiments, discussed above are merely exemplary and are not meant to be limiting. Any suitable method for determining a stable period of exhalation may be utilized by the present disclosure.

During a constant ST mode, if the trigger module 115 determines a stable portion of exhalation, the trigger module 115 sets the initial probability (also referred to as a priori probability) for each hypothesis and calculates a mean parameter signal. During a constant ST mode, if the trigger module 115 determines that the parameter signal of the current computational cycle is in an active portion of exhalation, the trigger module 115 continues to monitor the parameter signal and to check for the stable portion of exhalation.

In some embodiments, the trigger module 115 utilizing a constant ST mode assumes that the null hypothesis is true and that the trigger hypothesis is false when setting the initial probability for each of the null hypothesis and the trigger hypothesis for the parameter signal from the first computational cycle of the exhalation. For example, the trigger module 115 may utilize the following equations: F _(k,i)=π_(i)  (1) F _(0,1)=1  (2) F _(0,2)=0  (3) where

-   -   F_(k,i) is the probability of hypothesis i at t_(k);     -   π_(i) is the a priori probability of hypothesis i at t=0;     -   t is time;     -   i is the current hypothesis; and     -   k is the current computation cycle.         The trigger module 115 utilizing a constant ST mode calculates         the mean parameter signal based on a predetermined set of         parameter signals for a most recent set of computational cycles         since the parameter signal of the null hypothesis is assumed to         be constant from one computational cycle to another. For         example, the trigger module 115 may utilize the following         equation to calculate the mean parameter signal:

$\begin{matrix} {\overset{\_}{x} = {\frac{1}{N}{\sum\limits_{k = 1}^{N}{\overset{\sim}{x}}_{k}}}} & (4) \end{matrix}$ where

-   -   x is the mean parameter;     -   N is the total number of parameter signals utilized; and     -   {tilde over (x)}_(k) is a measurement of the signal at t_(k)         The trigger module 115 utilizing a constant ST mode sets the         initial probability for each of the null hypothesis and the         trigger hypothesis by adding a hypothesis transition probability         to a probability of a previous state for each of the null         hypothesis and the trigger hypothesis. For example, the trigger         module 115 may utilize the following equation to calculate or         set the initial probability for each of the null hypothesis and         the trigger hypothesis for the current computational cycle:         P _(k,i)({tilde over (X)} _(k−1))=F _(k−1,i) +{tilde over (p)}         _(i)(1−F _(k−1,i))  (5)         where     -   P_(k,i)({tilde over (X)}_(k−1)) is an a priori probability of         hypothesis i at t_(k) given measurement history {tilde over         (X)}_(k−1);     -   {tilde over (p)}_(i) is a a priori probability of transition to         hypothesis i from t_(k) to t_(k+1); and     -   F_(k,i) is a probability of hypothesis i at t_(k).         In some embodiments, the trigger module 115 utilizing the         constant ST mode calculates the hypothesis transition         probability based on a respiration rate and a sampling         frequency. For example, the trigger module 115 may utilize the         following equation to calculate the hypothesis transition         probability:

$\begin{matrix} {{\overset{\sim}{p}}_{i} = \frac{RR}{60f}} & (6) \end{matrix}$

where

-   -   RR is a respiratory rate (breaths per minute); and     -   f is a sampling frequency (Hz).         In alternative embodiments, the probability of transition         increases as exhalation time (t) approaches t=60/RR, allowing         the trigger module 115 to react more aggressively as exhalation         time approaches the average time between breaths.

The trigger module 115 utilizing the constant ST mode also calculates or updates the noise estimate based at least on the mean parameter signal. In some embodiments, this is accomplished by the trigger module 115 calculating the standard deviation for all the parameter signals for each measured computational cycle taken so far during the exhalation. For example, the trigger module 115 may utilize the following equation to calculate the noise estimate:

$\begin{matrix} {\sigma = \sqrt{\frac{1}{1 - N}{\sum\limits_{k = 1}^{N}\left( {{\overset{\sim}{x}}_{k} - \overset{\_}{x}} \right)^{2}}}} & (7) \end{matrix}$

where

-   -   {tilde over (x)}_(k) is a measurement of the signal at t_(k);         and     -   σ is the standard deviation (or noise estimate).         The trigger module 115 utilizing the constant ST mode also         calculates a residual for the null hypothesis and the trigger         hypothesis based at least on the parameter signal for the         current computational cycle. For example, the trigger module 115         may utilize the following generalized equation to calculate the         residual for each hypothesis:         R _(i)(x _(k))={tilde over (x)} _(k) −H _(k,i)  (8)

where

-   -   R_(i)(x_(k)) is a residual of hypothesis i at t_(k); and     -   H_(k,i) A priori estimate of x at t_(k) for hypothesis i.         In some embodiments, the trigger module 115 utilizing the         constant ST mode calculates the residual for the null hypothesis         by subtracting the mean value for the null hypothesis from the         parameter signal for the current computational cycle. For         example, the trigger module 115 may utilize the following         equation to calculate the residual for the null hypothesis:         R ₁(x _(k))={tilde over (x)} _(k) −x   (9)         In further embodiments, the trigger module 115 utilizing the         constant ST mode calculates the residual for the trigger         hypothesis by subtracting a predicted mean value for the trigger         hypothesis from the parameter signal for the current         computational cycle. In some embodiments, the predicted mean         value for the trigger hypothesis is calculated by the trigger         module 115 by subtracting a clinician-specified trigger setting         with the mean value of the signal. For example, the trigger         module 115 may utilize the following equation to calculate the         residual for the trigger hypothesis:         R ₂(x _(k))={tilde over (x)} _(k)−( x−b)  (10)

where

-   -   b is a breath trigger setting.

Next, the trigger module 115 utilizing the constant ST mode calculates a first probability for the null hypothesis for the parameter signal for the current computational cycle based on the initial probability for the null hypothesis, the noise estimate, and the residual for the null hypothesis. The trigger module 115 utilizing the constant ST mode also calculates a second probability for the trigger hypothesis for the parameter signal for the current computational cycle based on the initial probability for the trigger hypothesis, the noise estimate, and the residual for the trigger hypothesis. In some embodiments, the trigger module calculates the first and second probabilities by calculating a probability density function for each of the null hypothesis and the trigger hypothesis and by multiplying the probability density function for each hypothesis with its corresponding initial probability and normalizing. For example, the trigger module 115 may utilize the following equation to calculate the probability density function for each of the hypotheses:

$\begin{matrix} {{f_{i}\left( x_{k} \right)} = \left( \frac{1}{\sqrt{2\pi}\sigma} \right)^{(\frac{{R_{i}{(x_{k})}}^{2}}{2\sigma^{2}})}} & (11) \end{matrix}$

where

-   -   ƒ_(i)(x_(k)) is a probability density function of x given         hypothesis i.         In another example, the trigger module 115 may utilize the         following equation to calculate the first and second         probabilities (e.g., multiplying the probability density         function with its corresponding initial probability and         normalizing):

$\begin{matrix} {F_{k,i} = \frac{{P_{k,i}\left( {\overset{\sim}{X}}_{k - 1} \right)} \cdot {f_{i}\left( x_{k} \right)}}{\sum\limits_{i = 0}^{m}{{P_{k,i}\left( {\overset{\sim}{X}}_{k - 1} \right)} \cdot {f_{i}\left( x_{k} \right)}}}} & (12) \end{matrix}$

where

-   -   F_(k,i) is a probability of hypothesis i at t_(k).

During a variable ST mode, if the trigger module 115 determines that the parameter signal for a current computational cycle is in an active portion of exhalation, the trigger module 115 continues to monitor the parameter signal and to check for the stable portion of exhalation. During a variable ST mode, if the trigger module 115 determines that the parameter signal for the current computational cycle is in a stable portion of exhalation, the trigger module 115 calculates an initial predicted parameter signal (also referred to herein as a priori estimate at current computational cycle (t_(k))) and an initial covariance (also referred to herein as an a priori estimate of covariance at t_(k)) for the next computational cycle and calculates a post predicted signal (also referred to herein as a posterori estimate at t_(k)) and a post covariance rate (also referred to herein as a posterori estimate of covariance for at t_(k)) for the next computational cycle. The covariance for a predicted signal is the estimated uncertainty for the predicted parameter. For example, the covariance for the predicted signals illustrated in FIG. 11 can be used to construct the error bounds shown in graph 1102, 1104, or 1106.

In some embodiments, an initial confidence rate for the initial predicted parameter signal and a post confidence rate for the post predicted signal are determined by the trigger module 115. The confidence rate is a quantification of how likely a predicted signal will match the parameter signal of the next computational cycle. In some embodiments, the confidence rate for a predicted signal is directly dependent upon the calculated covariance for the predicted signal. For example, the initial confidence rate may be determined based on the a priori estimate of covariance. In another example, the post confidence rate may be determined based on a posterori estimate of covariance. The higher the covariance for a predicted signal, the lower the confidence rate for the predicted signal. The lower the covariance for a predicted signal, the higher the confidence rate for the predicted signal.

In some embodiments, the trigger module 115 utilizing a variable ST mode assumes that the null hypothesis is true and that the trigger hypothesis is false when calculating the initial predicted parameter signal and the post predicted parameter signal for the parameter signal of the first computational cycle of the exhalation. In some embodiments, the trigger module 115 utilizing a variable ST mode calculates the initial predicted parameter signal for the next computational cycle and the initial covariance for the initial predicted parameter signal for the parameter signal (or true state of the measurement (x)) for the current computational cycle. In further embodiments, the trigger module 115 calculates the post predicted parameter signal for the next computational cycle and the post covariance for the post predicted parameter signal for a first derivative (velocity state ({dot over (x)})) and a second derivative (acceleration state ({umlaut over (x)})) of the parameter signal for the current computational cycle. In other words, the trigger module 115 analyzes the actual signal parameter (x), the first derivative of the signal parameter ({dot over (x)}), and second derivative of the signal parameter ({umlaut over (x)}) for a current computational cycle to predict the signal parameter for the next computational cycle for the null hypothesis and the trigger hypothesis. For example, the three different states are shown in the equation below:

$\begin{matrix} {X_{k} = \begin{bmatrix} x \\ \overset{.}{x} \\ \overset{¨}{x} \end{bmatrix}} & (13) \end{matrix}$ Within a breath, the trigger module 115 utilizing the variable ST mode assumes that the true acceleration state is not time-varying. As such, the trigger module 115 estimates the true value of the acceleration state. For example, the trigger module 115 may utilize the following state transition matrix:

$\begin{matrix} {\Phi = \begin{bmatrix} 1 & t & {\frac{1}{2}t^{2}} \\ 0 & 1 & t \\ 0 & 0 & 1 \end{bmatrix}} & (14) \end{matrix}$ In some embodiments, at t=0, both the initial covariance estimate (M) and post covariance estimate (P) are constructed by the trigger module 115 using measurement noise (υ) to define the diagonals, ignoring the coupled covariance values, an example of which is illustrated below:

$\begin{matrix} {M = {P = \begin{bmatrix} \upsilon & 0 & 0 \\ 0 & \frac{2\upsilon}{\Delta\; t} & 0 \\ 0 & 0 & \frac{2\upsilon}{\Delta\; t^{2}} \end{bmatrix}}} & (15) \end{matrix}$ In order to prevent the filter from collapsing, a small amount of process noise (V) is injected at each time step by the trigger module 115. For example, the structure of this process noise may be defined as follows:

$\begin{matrix} {V = \begin{bmatrix} v_{p} & 0 & 0 \\ 0 & v_{v} & 0 \\ 0 & 0 & v_{a} \end{bmatrix}} & (16) \end{matrix}$ In some embodiments, the trigger module 115 utilizes Kalman Tracking Filter equations to calculate the initial predicted parameter signal at the initial covariance and the post predicted signal at the post covariance for the next computational cycle. For example, the trigger module 115 may utilize the following Kalman Tracking Filter equations to calculate the initial predicted parameter signal at the initial covariance and the post predicted signal at the post covariance for the next computational cycle: {tilde over (x)} _(k) =HX _(k)+υ  (17) X _(k+1) =Φ{circumflex over (X)} _(k)  (18) M _(k+1) =ΦP _(k)Φ^(T) +V  (19) K _(k+1) =M _(k+1) H ^(T)(HM _(k+1) H ^(T) +V)⁻¹  (20) R _(k+1) =HX _(k+1) −{tilde over (x)} _(k+1)  (21) {circumflex over (X)} _(k+1) =X _(k+1) −K _(k+1) R _(k+1)  (22) P _(k+1) =M _(k+1) −K _(k+1) HM _(k+1) ^(T)  (23)

where

-   -   {tilde over (x)}_(k) is a measurement of the signal at t_(k);     -   H is an observability matrix of state X;     -   X_(k) is a True state at t_(k);     -   υ is a measurement noise;     -   X _(k) is an a priori estimate of state of at t_(k);     -   Φ is a state transition matrix from t_(k) to t_(k+1);     -   {circumflex over (X)}_(k) is a posterori estimate of state at         t_(k);     -   M_(k) is an a priori estimate of covariance at t_(k);     -   P_(k) is a posterori estimate of covariance at t_(k);     -   V is a process noise;     -   K_(k+1) is a Kalman gain at t_(k+1); and     -   R_(k+1) is a residual at t_(k+1).         In some embodiments, the variable ST mode utilizes the states         and covariance of the Kalman Tracking Filter instead of the         initial mean parameter signal used by the trigger module 115         during a constant ST mode to calculate the probability of the         null hypothesis and/or trigger hypothesis being true.

After the trigger module 115 utilizing the variable ST mode calculates the predicted signal parameters (e.g., the initial and the post), the trigger module 115 determines if it is ready to calculate the first and second probabilities based on a predetermined run threshold. The predetermined run threshold may be determined based on user input or by the ventilator based on ventilator and patient parameters. In some embodiments, the run threshold is a minimum exhalation time. For example, the minimum exhalation time may be an exhalation time of at least 50 ms. In other examples the minimum exhalation may be an exhalation time of at least 60 ms, 70 ms, 75 ms, 80 ms, 90 ms, 100 ms, 125 ms, 150 ms, 175 ms, 200 ms, and etc. This list is exemplary and is not meant be limiting. Any suitable minimum exhalation time as would be known by a person of skill in the art may be utilized as the run threshold by the trigger module 115. In alternative embodiments, the predetermined run threshold is a maximum calculated covariance for the initial predicted signal parameter and/or the post predicted signal parameter. Any suitable maximum covariance limit as would be known by a person of skill in the art may be utilized at the run threshold by the trigger module 115. In other embodiments, the predetermined run threshold is a minimum confidence rate for the initial predicted signal parameter and/or the post predicted signal parameter. For example, the minimum confidence rate may be a confidence rate of at least 50%. In other examples the minimum confidence rate may be at least 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 98%, 99%, and etc. This list is exemplary and is not meant be limiting. Any suitable minimum confidence rate as would be known by a person of skill in the art may be utilized at the run threshold by the trigger module 115.

During a variable ST mode, if the trigger module 115 determines that the run threshold has been met, the trigger module 115 calculates the probability for each of the hypotheses. During a variable ST mode, if the trigger module 115 determines the run threshold has not been met, the trigger module 115 continues to monitor the parameter signal and to calculate the initial and post predicted values (if a stable portion of exhalation is found).

The trigger module 115 utilizing the variable ST mode also calculates or updates the noise estimate based at least on a single value of the covariance to determine at least one of the initial covariance and the post covariance. In some embodiments, this is accomplished by the trigger module 115 calculating the square root of P(1,1) in equation #15 listed above. In some embodiments, this is accomplished by the trigger module 115 by utilizing the measurement noise (υ).

The trigger module 115 utilizing the variable ST mode also calculates a residual for the null hypothesis and the trigger hypothesis based at least on the parameter signal for the current computational cycle. In some embodiments, the trigger module 115 utilizing the variable ST mode calculates the residual for the null hypothesis by subtracting the initial predicted value from the parameter signal for the current computational cycle. For example, the trigger module 115 may utilize the following equation to calculate the residual for the null hypothesis: R ₁(x _(k))={tilde over (x)} _(k) −X _(k)(1)  (24) where

-   -   (1) is cell number 1 of the vector.         In further embodiments, the trigger module 115 utilizing the         variable ST mode calculates the residual for the trigger         hypothesis by subtracting an initial predicted parameter signal         for the trigger hypothesis from the parameter signal for the         current computational cycle. For example, the trigger module 115         may utilize the following equation to calculate the residual for         the trigger hypothesis:         R ₂(x _(k))={tilde over (x)} _(k)−( X _(k)(1)−b)  (25)

Next, the trigger module 115 utilizing the variable ST mode calculates a first probability for the null hypothesis for the parameter signal for the current computational cycle based on the initial predicted parameter signal for the null hypothesis, the noise estimate, and the residual for the null hypothesis. The trigger module 115 utilizing the variable ST mode also calculates a second probability for the trigger hypothesis for the parameter signal for the current computational cycle based on the initial predicted parameter signal for the trigger hypothesis, the noise estimate, and the residual for the trigger hypothesis. In some embodiments, the trigger module 115 calculates the first and second probabilities by calculating a probability density function for each of the null hypothesis and the trigger hypothesis and by multiplying the probability density function for each hypothesis with its corresponding predicted signal parameter and normalizing. For example, the trigger module 115 may utilize equation #11 to calculate the probability density function for each of the hypotheses. In another example, the trigger module 115 may utilize equation #12 (e.g., multiplying the probability density function with its corresponding predicted signal parameter and normalizing) to calculate the first and second probabilities.

Once the probabilities for the null and trigger hypothesis are calculated, the trigger module 115 utilizing either type of ST mode compares each probability to a trigger threshold. If the trigger module 115 determines that null hypothesis meets the trigger threshold, the trigger module 115 does not detect a patient effort and monitors the parameter signal for the next computational cycle. If the trigger module 115 determines that trigger hypothesis meets the trigger threshold, the trigger module 115 detects a patient effort and triggers inspiration ending exhalation. The trigger threshold, as discussed above, may be selected by a user or selected by the ventilator based on ventilator and patient parameters. In some embodiments, the trigger threshold is a minimum probability. For example, the minimum probability may be at least 50%. In another example, the minimum probability may be at least 95%. In other examples, the minimum probability is at least 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 98%, 99%, and etc. This list is exemplary and is not meant be limiting. Any suitable minimum probability as would be known by a person of skill in the art may be utilized as the trigger threshold by the trigger module 115.

In other embodiments, the ventilator 100 is preconfigured to deliver an inspiration after a predetermined amount of exhalation time to prevent a patient 150 from becoming under-ventilated. Accordingly, the predetermined amount of exhalation time (e.g., known as an apnea interval in some ventilators) may also be a trigger threshold in this embodiment. For example, the trigger module 115 will automatically trigger an inspiration after 20 seconds, 30 seconds, or 60 seconds of exhalation time. In some embodiments, the predetermined amount of time is determined by the clinician and/or ventilator 100 based on whether the patient 150 is an infant, child, adult, male, female, and/or suffering from a specific disease state.

If the trigger module 115 determines that ventilator and/or patient parameters meet and/or exceed an inspiration trigger threshold during exhalation, the trigger module 115 instructs the inspiratory module 104 to deliver an inspiration, which effectively ends the exhalation phase. If the trigger module 115 determines that ventilator and/or patient parameters does not meet and/or exceed an inspiration trigger threshold during exhalation, the trigger module 115 continues to monitor the ventilator and/or patient parameters and compare them to a trigger threshold until the ventilator and/or patient parameters meet and/or exceed a trigger threshold.

As discussed above, each of the two kinds of the ST mode decreases the ventilator's trigger detection response time. In one embodiment, where the constant ST mode was utilized to ventilate a patient with chronic obstructive pulmonary disease (COPD), the constant ST mode decreased the ventilator's trigger detection time by about 0.080 seconds when compared to a conventional flow triggering type, as illustrated in FIG. 6. In another embodiment, where the variable ST mode was utilized to ventilate a patient with lung trauma, the variable ST mode decreased the ventilator's trigger detection time by about 0.181 seconds when compared to a conventional flow triggering type, as illustrated in FIG. 9.

FIG. 7 illustrates an embodiment of a graph 702 of pressure versus time for an exhalation with a constant PEEP and illustrates a graph 704 of the calculated null and trigger probabilities by a constant ST mode versus time for the same exhalation. As shown in graph 704 of FIG. 7, the probability of the null hypothesis remains at 100% until just before 421.6 seconds. Similarly, the probability of the trigger hypothesis remains at 0% until just before 421.6 seconds, as illustrated in graph 704 of FIG. 7. As is logical, the probability of the null hypotheses being true goes down as the probability of the trigger hypothesis being true increases. In this embodiment, the constant ST mode detected a patient trigger or the patient's inspiratory effort when the probability of the trigger hypothesis became larger than the 99% minimum probability trigger setting.

FIG. 8 illustrates an embodiment of a graph 802 of pressure versus time for an exhalation with a substantially constant PEEP and illustrates a graph 804 of the calculated null and trigger probabilities by a constant ST mode versus time for the same exhalation. As shown in graph 804 of FIG. 8, the probability of the null hypothesis remains at 100% until just before 69 seconds, where a slight change in the probability is detected based a slight change in the pressure signal parameter. Similarly, the probability of the trigger hypothesis remains at 0% until just before 69 seconds, as illustrated in graph 804 of FIG. 8. The noise estimate of the constant ST mode prevents the ventilator from detecting a false trigger based on this slight change in the signal parameter just before 69 seconds. However, both probabilities did not significantly change until after 69 seconds when the pressure signal changed more significantly. In this embodiment, the constant ST mode detected a patient trigger or the patient's inspiratory effort when the probability of the trigger hypothesis became larger than the 99% minimum probability trigger threshold at around 69.62 seconds.

FIG. 10 illustrates an embodiment of a graph 1002 of pressure versus time for an exhalation with a descending PEEP and illustrates a graph 1004 of the calculated trigger probability by a variable ST mode versus time for the same exhalation. As shown in graph 1004 of FIG. 10, the probability of the trigger hypothesis remains at 0% until just before 324 seconds, as illustrated in graph 1004 of FIG. 10. In this embodiment, the variable ST mode detected a patient trigger or the patient's inspiratory effort when the probability of the trigger hypothesis became larger than the 99% trigger setting at 324 seconds. FIG. 11 illustrates an embodiment of a graph showing the predicted signal and its uncertainty bounds for the measured pressure signal 1102, a graph showing the predicted signal and its uncertainty bounds for the first derivative of the measured pressure signal 1104, and a graph showing the predicted signal and its uncertainty bounds for the second derivative of the measured pressure signal 1106 for the same exhalation as illustrated in FIG. 10. In some embodiments, the covariance for the measured pressure signal illustrated in graph 1102 is calculated based on known pressure sensor errors. As illustrated by graphs 1102, 1104, and 1106, the covariance of the second derivative of the parameter signal is larger than the covariance for the first derivative of the pressure signal and is larger than the covariance for the measured pressure signal until 322.5 seconds is reached. For example, the run threshold of the variable ST mode may be met for the exhalation illustrated in FIGS. 10 and 11, when the covariance drops below a threshold limit or when a confidence rate calculated based on the covariance is over 90% for the first derivative and/or the second derivative as illustrated at 322.5 seconds.

As discussed above, the ventilator 100 may also include a potential trigger module 118. The potential trigger module 118 receives a potential trigger setting (also referred to as a potential trigger threshold) for the utilized breath type. The potential trigger setting is any trigger threshold that is different than the active trigger setting for the current breath type being utilized by the trigger module 115 to detect patient efforts and trigger inspiration. For example, in some embodiments, the active trigger setting and the potential trigger setting are two different flow triggering thresholds. In other embodiments, the active trigger setting and the potential trigger setting are two different pressure triggering thresholds. In additional embodiments, the active trigger setting and the potential trigger setting are two different intrapleural pressure triggering thresholds. In further embodiments, the active trigger setting and the potential trigger setting are two different neural triggering thresholds. In other embodiments, the active trigger setting and the potential trigger setting are two different probability triggering thresholds. As would be understood by a person of skill in the art, the active and potential trigger settings may be any known type of triggering threshold for any known breath type.

The potential trigger module receives the potential trigger setting from user input or selection. In some embodiments, the potential trigger module 118 is activated by user input or selection. For example, the operator of the ventilator 100 may turn on or activate a potential trigger application, which in turn activates or turns on the potential trigger module 118. In some embodiments, the potential trigger module receives a potential trigger from user input only after a potential trigger application has been activated by the operator.

In additional embodiments, the application, trigger module 118, or ventilator 100 tests a series of potential triggers. In some embodiments, the operator selects a potential trigger setting for implementation by the ventilator based on the displayed potential trigger and active trigger information. In alternative embodiments, a potential trigger setting for implementation is selected automatically by the application, trigger module 118, or ventilator 100 based on the testing of the series of potential triggers. In some embodiments, the ventilator 100, trigger module 118, or application picks a trigger setting with the best improvement in trigger time while minimizing false triggers from the series of potential triggers tested.

The potential trigger module 118 and the trigger module 115 monitor respiratory data (including one or more parameter signals) with one or more sensors 107. The respiratory data may be any monitored parameter signal or parameters derived therefrom. As discussed above, the trigger module 115 detects a patient effort when the monitored respiratory data breaches the active trigger setting and triggers or delivers inspiration based on this detected patient effort. The detected patient effort utilized by the trigger module 115 to deliver inspiration is referred to herein as the active patient trigger. The potential trigger module 118 monitors the same respiratory data as the trigger module 115 but compares the respiratory data to the received potential threshold to detect the patient effort. The potential trigger module 118 detects a potential patient trigger when the respiratory data breaches the potential trigger setting. However, the potential trigger module 118 does not deliver or trigger inspiration based on the detected potential patient trigger, but instead displays information based on one or more detected potential patient triggers and one or more detected active patient triggers.

In some embodiments, the monitored parameter signals are saved in storage 114. In these embodiments, the potential trigger module 118 upon activation may post-process these saved signals to determine past potential patient triggers. Accordingly, in these embodiments, the saved data or a subset of the saved monitored parameter signals can be analyzed for potential triggers by the potential trigger module 118 and presented to the clinician on the display 122 upon selection of the potential patient trigger.

In some embodiments, the potential trigger module 118 sends instructions to the display 122 to display a potential trigger indicator on a patient waveform where each potential patient trigger is detected. In further, embodiments, the trigger module 115 and/or the potential trigger module 118 may also send instructions to the display 122 to display an active indicator on the same patient waveform where each active patient trigger was detected that triggers the delivery of inspiration by the ventilator 100. FIGS. 12 and 13 illustrate an embodiment of a patient waveform 1200 (e.g., flow waveform) and 1300 (e.g. pressure waveform) displaying active indicators 1202 and potential indicators 1204. The display of the potential indicator 1204 in relation to the active indicators 1202 allow an operator to easily and confidently decide if the potential trigger setting increases or decreases the speed of trigger detection. Further, the display of the potential indicator 1204 in relation to the active indicators 1202 may also allow an operator to easily and confidently decide if the potential trigger threshold increases or decreases the number of false triggers for the delivered breath type. For example, as illustrate in FIG. 13, a potential trigger is illustrated with the potential indicator 1204 a immediately after the start of exhalation. An operator of the ventilator would see this potential indicator 1204 a and would immediately know that this detected trigger occurred too close to the beginning of exhalation and is most likely a false trigger. The presenting of this detected false trigger would inform the operator that the selected potential trigger threshold illustrated in FIG. 13 is likely too sensitive for use with this breath type and patient.

In additional embodiments, the potential trigger module 118 compares the detected one or more active patient triggers with the one or more determined patient efforts. Based on this comparison, the potential trigger module 118 may determine or calculate difference statistics 1306. The difference statistics 1306 is data about the difference between the detected active trigger and the potential trigger. For example, the potential trigger module 118 may send instructions to the display 122 to display the average time difference between the two different triggers along with a calculated standard deviation for the time difference and the number of delivered breaths that generated the average time difference as illustrated in FIG. 13. The potential trigger module 118 may send instructions to the display 122 to display just the difference statistics 1306 or may send instructions to the display 122 to display the difference statistics 1306 in combination with a potential indicator 1204 and active indicator 1202 on a patient waveform 1200. In further embodiments, the potential trigger module 118 may generate a difference graph based on the difference statistics and one or more different potential trigger settings. In these embodiments, the potential trigger module 118 may send instructions to the display 122 to display the difference graph. FIG. 14 illustrates an example embodiment of a difference graph 1400 illustrating the potential decrease in triggering times for different potential trigger settings. Graph 1400 further illustrates an increase in the standard deviation 1402 for the trigger improvement times as the trigger sensitivity increases, which could be the direct result of an increase in false triggers. As such, an operator may avoid implementing potential trigger thresholds that exhibit large standard deviations.

In alternative embodiments, the potential trigger module 118 receives two or more potential trigger thresholds, which is then utilized to detect multiple potential triggers. The potential trigger module 118 may analyze and display these additional potential triggers at the same time. The display of multiple potential triggers at once allows a clinician to quickly and easily pick the best potential trigger out of several choices for implementation.

In some embodiments, the display 122 on the ventilator 100 is a graphical user interface for displaying respiratory data. The ventilator 100 is configured with a computer having a user interface including the graphical user interface for accepting commands and for displaying the respiratory data. In these embodiments, the graphical user interface includes or displays graphical representations of respiratory data, one or more potential trigger indicators, and/or one or more active trigger indicators. In further embodiments, the graphical user interface further includes or displays difference statistics between the potential trigger indicator and the active breath indicator and/or graphs based on the difference statistics.

In additional embodiments, the ventilator 100 provides a graphical user interface for accepting commands and for displaying respiratory data. In these embodiments, the ventilator 100 includes at least one display device 122, a trigger module 115 that determines active patient triggers based on an active trigger setting for a given breath type, a potential trigger module 118 that determines potential patient triggers based on a potential trigger setting for the same breath type, and at least one memory 112, communicatively coupled to the at least one processor 116. The memory 112 contains instructions that, when executed by a processor of the ventilator 100, provide a graphical user interface on the at least one display 122. The graphical user interface includes or displays graphical representations of respiratory data, one or more potential trigger indicators, and one or more active breath indicators. In further embodiments, the graphical user interface further includes or displays difference statistics between the potential trigger indicator and the active breath indicator and/or graphs based on the difference statistics.

FIGS. 2A and 2B illustrate embodiments of a method 200 for triggering inspiration during ventilation of a patient on a ventilator utilizing a ST mode. FIG. 2A illustrates an embodiment of a method 200 a for triggering inspiration during ventilation of a patient on a ventilator utilizing a constant ST mode. FIG. 2B illustrates an embodiment of a method 200 b for triggering inspiration during ventilation of a patient on a ventilator utilizing a variable ST mode.

Both methods 200 a and 200 b begin at the start of exhalation. As illustrated, both methods 200 a and 200 b include a monitoring operation 202. During the monitoring operation 202, the ventilator monitors ventilator and/or patient parameter signals. As used herein, ventilator parameters include any parameter determined by the operator and/or ventilator. As used herein, patient parameters include any parameter that is not determined by the ventilator and/or operator. In some embodiments, the ventilator during the monitoring operation 202 monitors sensor measurements and/or parameters derived or calculated from the sensor measurements. In further embodiments, the ventilator during the monitoring operation 202 monitors sensor measurements from other monitoring devices coupled to the ventilator, such as an oximeter or a capnograph. In some embodiments, the ventilator during the monitoring operation 202 monitors exhalation time, exhalation volume, exhalation flow rate, exhalation pressure, and/or intrapleural pressure. Sensors suitable for this detection may include any suitable sensing device as known by a person of skill in the art for a ventilator, such as an expiration flow sensor.

Further, both methods 200 a and 200 b include decision operation 204. During the decision operation 204, the ventilator determines if a stable portion of exhalation is detected. The ventilator during decision operation 204 may utilize any suitable method and/or parameter signal for determining if a stable portion of exhalation is detected. Several examples of which are discussed above. If the ventilator determines that exhalation is not stable during decision operation 204, the ventilator continues to perform operation 202 and 204 for at least the next computation cycle. If the ventilator determines that the exhalation is stable during decision operation 204, the ventilator performs setting operation 208 when utilizing the stable ST mode and, alternatively, performs Kalman updating operation 205 when utilizing the variable ST mode.

As illustrated in FIG. 2A, method 200 a includes setting operation 208. The ventilator during setting operation 208 sets the initial probabilities for the null and trigger hypotheses and calculates the mean signal. In some embodiments, the ventilator during setting operation 208 assumes that the null hypothesis is true and that the trigger hypothesis is false when setting the initial probability for each of the null hypothesis and the trigger hypothesis for the parameter signal from the first computational cycle of the exhalation. For example, the ventilator during setting operation 208 may utilize equations #1, #2, and #3 to calculate the initial probabilities. In additional embodiments, the ventilator during setting operation 208 calculates the mean parameter signal based on a predetermined set of parameter signals for a most recent set of computational cycles since the parameter signal of the null hypothesis is assumed to be constant from one computational cycle to another. For example, the trigger module 115 may utilize equation #4 to calculate the mean parameter signal. In further embodiments, the ventilator during setting operation 208 sets the initial probability for each of the null hypothesis and the trigger hypothesis by adding a hypothesis transition probability to a probability of a previous state for each of the null hypothesis and the trigger hypothesis. For example, the ventilator during setting operation 208 may utilize equation #5 to calculate or set the initial probability for each of the null hypothesis and the trigger hypothesis for the parameter signal for the current computational cycle. In some embodiments, the ventilator during setting operation 208 calculates the hypothesis transition probability based on a respiration rate and a sampling frequency. For example, the ventilator during setting operation 208 may utilize equation #6 to calculate the hypothesis transition probability. In alternative embodiments, the probability of transition increases as exhalation time (t) approaches t=60/RR, allowing the ventilator during setting operation 208 to react more aggressively as exhalation time approaches the average time between breaths.

As illustrated in FIG. 2A, method 200 a also includes an updating operation 210. Updating operation 210 is performed by the ventilator after the mean signal is calculated during setting operation 208. The ventilator during updating operation 210 calculates or updates the noise estimate based at least on the calculated mean parameter signal. In some embodiments, this is accomplished by the ventilator during updating operation 210 by calculating the standard deviation for all the parameter signals for each measured computational cycle taken so far during the exhalation. For example, the ventilator during updating operation 210 may utilize equation #7 to calculate the noise estimate.

As illustrated in FIG. 2A, method 200 a also includes a calculating residuals operation 212. The calculating residuals operation 212 is performed by the ventilator after the initial probabilities for the null and trigger hypotheses are set during setting operation 208. The ventilator during calculating residuals operation 212 calculates a residual for the null hypothesis and the trigger hypothesis based at least on the parameter signal for the current computational cycle. For example, the ventilator during calculating residuals operation 212 may utilize generalized equation #8 to calculate the residuals for each hypothesis. In some embodiments, the ventilator during calculating residuals operation 212 calculates the residual for the null hypothesis by subtracting the mean value for the null hypothesis from the parameter signal for the current computational cycle. For example, the ventilator during calculating residuals operation 212 may utilize equation #9 to calculate the residual for the null hypothesis. In further embodiments, the ventilator during calculating residuals operation 212 calculates the residual for the trigger hypothesis by subtracting a predicted mean value for the trigger hypothesis from the parameter signal for the current computational cycle. For example, the ventilator during calculating residuals operation 212 may utilize equation #10 to calculate the residual for the trigger hypothesis.

Additionally, method 200 a also includes a calculating probabilities operation 214. The calculating probabilities operation 214 is performed by the ventilator after operations 208, 210, and 212 are performed by the ventilator. The ventilator during calculating probabilities operation 214 calculates a first probability for the null hypothesis for the parameter signal for the current computational cycle based on the initial probability for the null hypothesis, the noise estimate, and the residual for the null hypothesis. The ventilator during calculating probabilities operation 214 also calculates a second probability for the trigger hypothesis for the parameter signal for the current computational cycle based on the initial probability for the trigger hypothesis, the noise estimate, and the residual for the trigger hypothesis. In some embodiments, the ventilator during calculating probabilities operation 214 calculates the first and second probabilities by calculating a probability density function for each of the null hypothesis and the trigger hypothesis and by multiplying the probability density function for each hypothesis with its corresponding initial probability and normalizing. For example, the ventilator during calculating probabilities operation 214 may utilize the equation #11 to calculate the probability density function for each of the hypotheses. The ventilator during calculating probabilities operation 214 may utilize the equation #12 to calculate the first and second probabilities (e.g., multiplying the probability density function with its corresponding initial probability and normalizing.

As illustrated in FIG. 2B, method 200 b includes Kalman updating operation 205. The ventilator during Kalman updating operation 205 calculates an initial predicted parameter signal for the next computational cycle at an initial covariance for the parameter signal (or true state of the measurement (x)) for the current computational cycle. In further embodiments, the ventilator during Kalman updating operation 205 calculates the post predicted parameter signal for the next computational cycle at a post covariance for a first derivative (velocity state ({dot over (x)})) and a second derivative (acceleration state ({umlaut over (x)})) of the parameter signal for the current computational cycle. In other words, the ventilator during Kalman updating operation 205 analyzes the actual signal parameter (x), the first derivative of the signal parameter ({dot over (x)}), and second derivative of the signal parameter ({umlaut over (x)}) for a current computational cycle to predict the initial and post signal parameters for the next computational cycle for the null hypothesis and the trigger hypothesis.

Within a breath, the ventilator assumes that the true velocity and acceleration states are not time-varying. As such, the ventilator during Kalman updating operation 205 estimates the true value of them. For example, the ventilator during Kalman updating operation 205 may utilize the state transition matrix of equation #14. In some embodiments, at t=0, both the initial covariance estimate (M) and post covariance estimate (P) are constructed by the ventilator during Kalman updating operation 205 using measurement noise (υ) to define the diagonals, ignoring the coupled covariance values, an example of which is illustrated in equation #15. In order to prevent the filter from collapsing, a small amount of process noise is injected at each time step by the ventilator during Kalman updating operation 205. For example, the structure of this process noise may be defined as shown in equation #16.

In some embodiments, the ventilator during Kalman updating operation 205 utilizes the Kalman Tracking Filter equations to calculate the initial predicted parameter signal at the initial covariance and the post predicted signal at the post covariance for the next computational cycle. For example, the ventilator during Kalman updating operation 205 may utilize Kalman Tracking Filter equations #17 #18, #19, #20, #21, #22, and #23 to calculate the initial predicted parameter signal at the initial covariance and the post predicted signal at the post covariance for the next computational cycle. The ventilator during method 200 b utilizes the initial and post predicted signals instead of the initial probabilities as used by the ventilator during method 200 a.

Additionally, as illustrated in FIG. 2B, method 200 b includes a run decision operation 206. The run decision operation 206 is performed by the ventilator after the performance of Kalman updating operation 205. The ventilator during run decision operation 206 determines if it is ready to calculate the first and second probabilities for the null and trigger hypotheses based on a predetermined run threshold. The predetermined run threshold may be selected based on user input or selected by the ventilator based on ventilator and patient parameters. In some embodiments, the run threshold is a minimum exhalation time. For example, the minimum exhalation time may be an exhalation time of at least 50 ms. In alternative embodiments, the predetermined run threshold is a maximum covariance value for the initial predicted signal parameter and/or the post predicted signal parameter. For example, the maximum covariance value may be at most the square of the measurement sensor noise. In alternative embodiments, the predetermined run threshold is a minimum confidence rate calculated based on the covariance value for the initial predicted signal parameter and/or the post predicted signal parameter. For example, the minimum confidence rate may be confidence rate of at least 90%. If the ventilator during run decision operation 206 determines that the run threshold has been met, the ventilator performs updating noise operation 216. If the ventilator during run decision operation 206 determines the run threshold has not been met, the ventilator performs monitoring operation 202 and decision operation 204 for at least the next computational cycle.

Further, as illustrated in FIG. 2B, method 200 b includes an updating noise operation 216. The ventilator during updating noise operation 216 calculates or updates the noise estimate based at least on a single value of the covariance utilized in calculating at least one of the initial covariance and the post covariance. In some embodiments, this is accomplished by the ventilator during updating noise operation 216 by calculating the square root of P(1,1) in equation #15 listed above. In some embodiments, this is accomplished by the ventilator during updating noise operation 216 by calculating the square root of the measurement noise (υ).

As illustrated in FIG. 2B, method 200 b also includes a calculating residuals operation 218. Operation 218 is performed by the ventilator after the ventilator determines that the run threshold has been met during run decision operation 206. The ventilator during operation 218 calculates a residual for the null hypothesis and the trigger hypothesis based at least on the parameter signal for the current computational cycle. In some embodiments, the ventilator during operation 218 calculates the residual for the null hypothesis by differencing the parameter signal for the current computational cycle with the initial predicted value. For example, the ventilator during operation 218 may utilize equation #24 to calculate the residual for the null hypothesis. In further embodiments, the ventilator during operation 218 calculates the residual for the trigger hypothesis by subtracting an initial predicted parameter signal for the trigger hypothesis from the parameter signal for the current computational cycle. For example, the ventilator during operation 218 may utilize equation #25 to calculate the residual for the trigger hypothesis.

Additionally, method 200 b also includes a calculating probabilities operation 220. Operation 220 is performed by the ventilator after operations 205, 216, and 218 are performed by the ventilator. The ventilator during operation 220 calculates a first probability for the null hypothesis for the parameter signal for the current computational cycle based on the initial predicted parameter signal for the null hypothesis, the noise estimate, and the residual for the null hypothesis. The ventilator during operation 220 also calculates a second probability for the trigger hypothesis for the parameter signal for the current computational cycle based on the initial predicted parameter signal for the trigger hypothesis, the noise estimate, and the residual for the trigger hypothesis. In some embodiments, the ventilator during operation 220 calculates the first and second probabilities by calculating a probability density function for each of the null hypothesis and the trigger hypothesis and by multiplying the probability density function for each hypothesis with its corresponding predicted signal parameter and normalizing. For example, the ventilator during operation 220 may utilize equation #11 to calculate the probability density function for each of the hypotheses. In another example, the ventilator during operation 220 may utilize equation #12 (e.g., multiplying the probability density function with its corresponding predicted signal parameter and normalizing) to calculate the first and second probabilities.

Both methods 200 a and 200 b include a trigger decision operation 230. The ventilator during method 200 a performs trigger decision operation 230 after the performance of operation 214. The ventilator during method 200 b performs trigger decision operation 230 after the performance of operation 220. The ventilator during trigger decision operation 230 compares each probability to a trigger threshold. Based on this comparison, the ventilator during trigger decision operation 230 determines if the probability of the trigger hypothesis meets the trigger threshold. If the ventilator during trigger decision operation 230 determines that trigger hypothesis does not meet the trigger threshold, the ventilator performs operations 202 and 204 again at least for the next computation cycle. If the ventilator during trigger decision operation 230 determines that the trigger hypothesis meets the trigger threshold, the ventilator performs delivering operation 232. The trigger threshold, as discussed above, may be selected by a user or selected by the ventilator based on ventilator and patient parameters. In some embodiments, the trigger threshold is a minimum probability. For example, the minimum probability may be at least 98%. In another example, the minimum probability may be at least 99%.

Both methods 200 a and 200 b include a delivering operation 232. The ventilator during delivering operation 232 delivers an inspiration, which effectively ends the exhalation phase. In other words, operation 232 controls the ventilation (inspiration versus exhalation) delivered to the patient by the ventilator based on the result of the trigger decision operation 230. The inspiration provided to the patient may be determined by the ventilator and/or patient parameters. For example, the delivered inspiration may be based on a selected breath type or ventilation mode, such as volume control, pressure control, proportional assist, and etc.

In other embodiments, method 200 includes a display operation. The ventilator during the display operation displays any suitable information for display on a ventilator. In one embodiment, the display operation displays parameter signals, initial probabilities, mean parameter signals, noise estimates, residuals, calculated probabilities, trigger thresholds, run thresholds, predicted signal parameters, and etc.

In some embodiments, a microprocessor-based ventilator that accesses a computer-readable medium having computer-executable instructions for performing the method of ventilating a patient with a medical ventilator is disclosed. This method includes performing or repeatedly performing the steps disclosed in method 200 a or 200 b above and/or as illustrated in FIGS. 2A and 2B.

In some embodiments, the ventilator system includes: means for monitoring a parameter signal during an exhalation; means for determining a stable portion of exhalation based at least on the parameter signal for a current computational cycle; means for setting an initial probability for each of a null hypothesis and a trigger hypothesis based on a current exhalation time; means for calculating a mean of the parameter signal based on a predetermined set of parameter signals for a most recent set of computational cycles; means for updating a noise estimate based at least on the mean parameter signal; means for calculating a residual for the null hypothesis and the trigger hypothesis based at least on the parameter signal for the current computational cycle; means for calculating a first probability for the null hypothesis and calculating a second probability for the trigger hypothesis for the parameter signal for the current computational cycle based on the initial probability, the noise estimate, and the residual; means for comparing the first probability and the second probability to a threshold; and means for delivering inspiration when the second probability meets the threshold.

In some embodiments, the ventilator system includes: means for monitoring a parameter signal during an exhalation; means for determining a stable portion of exhalation based at least on the parameter signal for a current computational cycle; means for calculating an initial predicted parameter signal of the next computational cycle and an initial covariance for the initial predicted parameter signal for the parameter signal for the current computational cycle; means for calculating a post predicted parameter signal of the next computational cycle and a post covariance for the post predicted parameter signal for a first and second derivative of the parameter signal of the parameter signal for the current computational cycle; means for determining that a run threshold has been met based on at least one of a current exhalation time, the initial covariance, and the post covariance; means for updating a noise estimate based at least on a single covariance value utilized in calculating at least one of the initial covariance and the post covariance; means for calculating a residual for each of the null hypothesis and the trigger hypothesis based at least on the parameter signal for the current computational cycle; means for calculating a first probability for the null hypothesis and calculating a second probability for the trigger hypothesis for the parameter signal for the current computational cycle based at least on a predicted parameter signal, the noise estimate, and the residual; means for comparing the first probability and the second probability to a trigger threshold; and means for delivering inspiration when the second probability meets the threshold.

FIG. 3 illustrates an embodiment of a method 300 for analyzing potential trigger sensitivities and/or displaying potential trigger information during ventilation of a patient on a ventilator. Providing the operator with information about a potential trigger for a delivered breath type allows the operator to quickly and easily determine if a change to the trigger sensitivity would be beneficial to the patient. In some embodiments, the ventilator during method 300 is activated by user input or selection. For example, the operator of the ventilator may turn on or activate a potential trigger application, which in turn activates or turns on method 300.

Method 300 begins at the start of exhalation. As illustrated in FIG. 3 method 300 includes a receiving operation 302. The ventilator during receiving operation 302 receives one or more potential trigger settings. The one or more potential trigger settings are received from user input or selection. In some embodiments, ventilator during receiving operation 302 receives a potential trigger setting from user input only after a potential trigger application has been activated by the operator.

Further, method 300 includes a monitoring operation 304 as illustrated in FIG. 3. The ventilator during monitoring operation 304 monitors respiratory data (including one or more parameter signals). In some embodiments the ventilator during monitoring operation 304 monitors respiratory data with one or more sensors. The respiratory data may be any monitored parameter signal or parameters derived therefrom. The ventilator during monitoring operation 304 monitors the same respiratory data for the active trigger setting and the passive trigger setting. In further embodiments, the monitoring operation 304 stores the monitored respiratory data to form past respiratory data. The past respiratory data is any stored respiratory data from one or more previous or past computation cycles.

Additionally, method 300 includes an active detecting operation 306 as illustrated in FIG. 3. The ventilator during active detecting operation 306 detects active patient triggers based on the active trigger setting and the monitored respiratory data. In some embodiments, the ventilator during active detecting operation 306 delivers inspiration in response to a detected active patient trigger. In further embodiments, the ventilator during active detecting operation 306 stores the detected active patient triggers to form past active patient trigger. The past active patient triggers are any stored active patient triggers from one or more previous or past computation cycles.

As illustrated in FIG. 3, method 300 includes a potential detecting operation 308. The ventilator during the potential detecting operation 308 detects potential patient triggers based on each received potential trigger setting and the monitored respiratory data. In some embodiments, the ventilator during the potential detecting operation 308 detects a potential patient trigger when the respiratory data breaches the one or more potential trigger setting. However, the ventilator during the potential detecting operation 308 does not deliver or trigger inspiration based on any detected potential patient trigger. In some embodiments, the ventilator during potential detecting operation 308 retrieves the stored respiratory data and determines one or more past potential patient triggers based on the stored respiratory data. In further embodiments, the ventilator during potential detecting operation 308 retrieves the stored respiratory data in response to the activation of the potential trigger application and/or the receipt of a potential trigger. The past potential patient triggers are any determined past potential patient triggers from one or more previous or past computation cycles.

In some embodiments, method 300 includes an analyzing operation 310. The ventilator during the analyzing operation 310 compares one or more active patient triggers to the corresponding one or more potential patient trigger. Based on this comparison, the ventilator during the analyzing operation 310 determines difference statistics. The difference statistics, as discussed above, is data about the difference between the detected active patient trigger and the one or more potential patient triggers. In some embodiments, the difference statistics may include the time difference between the detected active patient trigger and the potential patient trigger, the average time difference between the two different triggers, a calculated standard deviation for the average time difference, and the number of delivered breaths that generated the average time difference. This list is exemplary and is not meant to be limiting. Any useful difference statistics may be determined by the ventilator during operation 310 as would be known by a person of skill in the art. In further embodiments, the ventilator during the analyzing operation 310 may determine or generate a graph of the difference statistics for one or multiple potential trigger settings.

In further embodiments, the ventilator during the analyzing operation 310 compares one or more past active patient triggers to the corresponding one or more past potential patient trigger. In these embodiments, based on this comparison, the ventilator during the analyzing operation 310 also determines difference statistics for the one or more past active patient triggers and the corresponding past potential patient triggers. In additional embodiments, the ventilator during the analyzing operation 310 may determine or generate a graph of the difference statistics for one or multiple past potential trigger settings and/or past active patient triggers. In other words, the ventilator during analyzing operation 310 post-processes stored respiration data to determine past potential triggers when the potential trigger module is activated and/or when a potential trigger is received.

As illustrated in FIG. 3, method 300 includes a displaying operation 312. The ventilator during the displaying operation 312 displays information based on one or more of the detected active patient triggers and on one or more of the detected potential patient trigger. In some embodiments, the ventilator during the displaying operation 312 displays one or more active indicators on a patient waveform where each active patient trigger is detected. In additional embodiments, the ventilator during the displaying operation 312 displays a potential indicator on the patient waveform where each potential patient trigger is detected for the one or more potential trigger settings. For example, FIGS. 12 and 13 illustrate an embodiment of a patient waveform 1200 (e.g., flow waveform) and 1300 (e.g. pressure waveform) displaying active indicators and potential indicators. In further embodiments, the ventilator during the displaying operation 312 displays difference statistics. For example, some exemplary difference statistics 1306 are displayed in FIG. 13. In some embodiments, the ventilator during the displaying operation 312 displays a graph of the difference statistics for one or multiple potential trigger settings for one or multiple potential trigger settings. For example, FIG. 14 illustrates an example embodiment of a difference graph 1400 showing the potential decrease in triggering times for different potential trigger settings. In further embodiments, the displaying information includes displaying information about one or more past active patient triggers and one or more past potential patient trigger.

In other embodiments, a microprocessor-based ventilator that accesses a computer-readable medium having computer-executable instructions for performing the method of ventilating a patient with a ventilator is disclosed. This method includes performing or repeatedly performing the steps disclosed in method 300 above and/or as illustrated in FIG. 3.

In some embodiments, the ventilator system includes: means for receiving an active trigger setting for a set breath type; means for receiving a potential trigger setting for the set breath type; means for monitoring respiratory data with at least one sensor; means for detecting active patient triggers based on the active trigger setting and the respiratory data; means for detecting at least one potential patient trigger based on the potential trigger setting and the respiratory data; and means for displaying information based on at least one active patient trigger and the at least one potential patient trigger.

Those skilled in the art will recognize that the methods and systems of the present disclosure may be implemented in many manners and as such are not to be limited by the foregoing exemplary embodiments and examples. In other words, functional elements being performed by a single or multiple components, in various combinations of hardware and software or firmware, and individual functions, can be distributed among software applications at either the client or server level or both. In this regard, any number of the features of the different embodiments described herein may be combined into single or multiple embodiments, and alternate embodiments having fewer than or more than all of the features herein described are possible. Functionality may also be, in whole or in part, distributed among multiple components, in manners now known or to become known. Thus, myriad software/hardware/firmware combinations are possible in achieving the functions, features, interfaces and preferences described herein. Moreover, the scope of the present disclosure covers conventionally known manners for carrying out the described features and functions and interfaces, and those variations and modifications that may be made to the hardware or software firmware components described herein as would be understood by those skilled in the art now and hereafter.

Numerous other changes may be made which will readily suggest themselves to those skilled in the art and which are encompassed in the spirit of the disclosure and as defined in the appended claims. While various embodiments have been described for purposes of this disclosure, various changes and modifications may be made which are well within the scope of the present technology as described. Numerous other changes may be made which will readily suggest themselves to those skilled in the art and which are encompassed in the spirit of the disclosure and as defined in the claims. 

What is claimed is:
 1. A method for ventilating a patient with a ventilator, comprising: monitoring a parameter signal during exhalation; determining a stable portion of exhalation based at least on the parameter signal for a current computational cycle; setting an initial probability for each of a null hypothesis and a trigger hypothesis based on a current exhalation time; calculating a mean of the parameter signal based on a predetermined set of parameter signals for a most recent set of computational cycles; updating a noise estimate based at least on the mean of the parameter signal; calculating a residual for the null hypothesis and the trigger hypothesis based at least on the parameter signal for the current computational cycle; calculating a first probability for the null hypothesis and calculating a second probability for the trigger hypothesis for the parameter signal for the current computational cycle based on the initial probability, the noise estimate, and the residual; comparing the first probability and the second probability to a threshold; and controlling ventilation delivered to the patient by the ventilator based on the comparison.
 2. The method of claim 1, wherein the controlling ventilation further comprising: determining that the first probability meets the threshold; and continuing to deliver the exhalation based on the determining that the first probability meets the threshold.
 3. The method of claim 1, wherein the controlling ventilation further comprising: determining that the second probability meets the threshold; and delivering inspiration based on the determining that the second probability meets the threshold.
 4. The method of claim 1, wherein setting the initial probability for each of the null hypothesis and the trigger hypothesis comprises: adding a hypothesis transition probability to a probability of a previous state for each of the null hypothesis and the trigger hypothesis to identify the initial probability for each of the null hypothesis and the trigger hypothesis.
 5. The method of claim 4, wherein the hypothesis transition probability is calculated based on a respiration rate and a sampling frequency.
 6. The method of claim 1, wherein the stable portion of exhalation is detected when at least one of the following conditions occur: when a slope of patient exhalation flow is about zero; and when (Max(P_(e))−Min(P_(e)))<1.5 cm H₂O) and (Max(Q_(e))−Min(Q_(e)))<1.5 LPM).
 7. The method of claim 1, wherein the noise estimate is determined by calculating a standard deviation for all parameter signals for each measured computational cycle during the exhalation.
 8. The method of claim 1, wherein calculating the residual for each of the null hypothesis and the trigger hypothesis comprises: subtracting a mean for the null hypothesis from the parameter signal for the current computational cycle; and subtracting a predicted mean value for the trigger hypothesis from the parameter signal for the current computational cycle.
 9. The method of claim 1, wherein calculating the first probability for the null hypothesis and calculating the second probability for the trigger hypothesis for the parameter signal for the current computational cycle based on the initial probability, the residual, and the noise estimate further comprises: calculating a probability density function for each of the null hypothesis and the trigger hypothesis; identifying the first probability for the parameter signal for the current computational cycle by multiplying the probability density function with the initial probability for the null hypothesis and normalizing; and identifying the second probability for the parameter signal for the current computational cycle by multiplying the probability density function with the initial probability for the trigger hypothesis and normalizing.
 10. The method of claim 1, wherein the threshold is a probability of greater than 99% received from user input. 